Biology subjects

Milanese, J.-S.

Publications and source records attributed to Milanese, J.-S..

3 recordsLinked to original sources

bioRxiv2022

Genome-wide CRISPR Screens Identify Ferroptosis as a Novel Therapeutic Vulnerability in Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is the most frequent cancer diagnosed in children. Despite the great progress achieved over the last 40 years, with cure rates now exceeding 85%, refractory or relapsed ALL still exhibit a dismal prognosis. This poor outcome reflects the lack of treatment options specifically targeting relapsed or refractory ALL. To address this gap, we have performed whole-genome CRISPR/Cas drop-out screens on a panel of seven B-ALL cell lines. Our results demonstrate that while there was a significant overlap in gene essentiality between ALL cell lines and other cancer types survival of ALL cell lines was dependent on several unique metabolic pathways, including an exquisite sensitivity to GPX4 depletion and ferroptosis induction and GPX4 KO. Detailed molecular analysis of B-ALL cells suggest that they are primed to undergo ferroptosis as they exhibit high steady-state oxidative stress potential, a low buffering capacity, and a disabled GPX-independent secondary lipid peroxidation detoxification pathway. Finally, we validated the sensitivity of B-ALL to ferroptosis induction using patient-derived B-ALL samples.

cancer biology↗

bioRxiv2018

Germline genomic landscapes of breast cancer patients significantly predict clinical outcomes

Germline genetic variants such as BRCA1/2 play an important role in tumorigenesis and clinical outcomes of cancer patients. However, only a small fraction (i.e., 5-10%) of inherited variants has been associated with clinical outcomes (e.g., BRCA1/2, APC, TP53, PTEN and so on). The challenge remains in using these inherited germline variants to predict clinical outcomes of cancer patient population. In an attempt to solve this issue, we applied our recently developed algorithm, eTumorMetastasis, which constructs predictive models, on exome sequencing data to ER+ breast (n=755) cancer patients. Gene signatures derived from the genes containing functionally germline genetic variants significantly distinguished recurred and non-recurred patients in two ER+ breast cancer independent cohorts (n=200 and 295, P=1.4x10-3). Furthermore, we found that recurred patients possessed a higher rate of germline genetic variants. In addition, the inherited germline variants from these gene signatures were predominately enriched in T cell function, antigen presentation and cytokine interactions, likely impairing the adaptive and innate immune response thus favoring a pro-tumorigenic environment. Hence, germline genomic information could be used for developing non-invasive genomic tests for predicting patients outcomes (or drug response) in breast cancer, other cancer types and even other complex diseases.

cancer biology↗

bioRxiv2018

eTumorMetastasis, a network-based algorithm predicts clinical outcomes using whole-exome sequencing data of cancer patients

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here we developed a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles, and identify network operational gene signatures (NOG signatures) which model the tipping point at which a tumor cell shifts from a state that doesnt favor recurrences to one that does. We showed that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the most recent common ancestor of the cells within a tumor) significantly distinguished recurred and non-recurred breast tumors. These results imply that somatic mutations of tumor founders are association with tumor recurrence and can be used to predict clinical outcomes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases.

bioinformatics↗